With development of computer technologies, technologies such as image denoising, enhancement, restoration, and segmentation by using a computer are widely applied.
In the field of digital images, an imaging process of a digital image is approximately modeled as a discrete convolution model, for example, a motion blur process caused by camera shake, a camera imaging defocus blur process, a low-definition image generation process, or the like. Therefore, enhancement and recovery of a digital image can be implemented by using a process inverse to the discrete convolution model, that is, a deconvolution technology. A typical deconvolution technology includes a digital image deblurring technology, an image super-resolution technology, and the like.
Wiener filtering is a classical deconvolution technology. In the Wiener filtering, a pseudo deconvolution kernel is introduced in a frequency domain, and a formula of the pseudo deconvolution kernel is as follows
                    W        =                                            F              ⁡                              (                x                )                                      _                                                                                                F                  ⁡                                      (                    k                    )                                                                              2                        +                          1              SNR                                                          (        1        )            where F(·) represents a Fourier transform, F(·) represents a complex conjugate of the Fourier transform, SNR represents a signal-to-noise power ratio (SNR), which achieves an effect of suppressing high-frequency noise of the pseudo deconvolution kernel, and k represents a convolution kernel. A sharp image may be expressed by using the following formulax=F−1(W·F(y))  (2)where y represents a blurry image, and F−1(·) represents an inverse Fourier transform. By using a convolution formula of the Fourier transform, formula (2) may be converted intox=F−1(W)=ω*y  (3)where ω is a deconvolution kernel in a space domain.
The introduction of the deconvolution kernel simplifies computation of a deconvolution algorithm, but the deconvolution kernel uses an SNR as a regularization constraint. Such a regularization constraint is affected by noise, so that an image recovery process using the deconvolution kernel is inevitably affected by noise and a ringing effect, which damages quality of a recovered sharp image to a certain extent.